Algorithms
Introduction to algorithms
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Randomized algorithms
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Handbook of Theoretical Computer Science: Formal Models and Semantics
Handbook of Theoretical Computer Science: Formal Models and Semantics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Optimal Mutation Rates in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
The science of breeding and its application to the breeder genetic algorithm (bga)
Evolutionary Computation
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
On the Choice of the Mutation Probability for the (1+1) EA
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Fitness Landscapes Based on Sorting and Shortest Paths Problems
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
On the Analysis of Evolutionary Algorithms - A Proof That Crossover Really Can Help
ESA '99 Proceedings of the 7th Annual European Symposium on Algorithms
Towards an analytic framework for analysing the computation time of evolutionary algorithms
Artificial Intelligence
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
Journal of Computer Science and Technology
Some theoretical results about the computation time of evolutionary algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A new approach to estimating the expected first hitting time of evolutionary algorithms
Artificial Intelligence
About the Time Complexity of Evolutionary Algorithms Based on Finite Search Space
Computational Intelligence and Security
About the Computation Time of Adaptive Evolutionary Algorithms
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Dynamic evolutionary optimisation: an analysis of frequency and magnitude of change
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A new approach to estimating the expected first hitting time of evolutionary algorithms
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A pheromone-rate-based analysis on the convergence time of ACO algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Theoretical analysis of rank-based mutation: combining exploration and exploitation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Analysis of the (1 + 1)-EA for finding approximate solutions to vertex cover problems
IEEE Transactions on Evolutionary Computation
When to use bit-wise neutrality
Natural Computing: an international journal
On the brittleness of evolutionary algorithms
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
Convergence time analysis for the multi-objective counting ones problem
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Theoretical results in genetic programming: the next ten years?
Genetic Programming and Evolvable Machines
Practical performance models of algorithms in evolutionary program induction and other domains
Artificial Intelligence
Towards analyzing recombination operators in evolutionary search
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Mutation rates of the (1+1)-EA on pseudo-boolean functions of bounded epistasis
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A time complexity analysis of ACO for linear functions
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
On the approximation ability of evolutionary optimization with application to minimum set cover
Artificial Intelligence
On algorithm-dependent boundary case identification for problem classes
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
When do evolutionary algorithms optimize separable functions in parallel?
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
Runtime analysis of evolutionary algorithms: basic introduction
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Evolutionary algorithms (EAs) are heuristic randomized algorithms which, by many impressive experiments, have been proven to behave quite well for optimization problems of various kinds. In this paper a rigorous theoretical complexity analysis of the (1 + 1) evolutionary algorithm for separable functions with Boolean inputs is given. Different mutation rates are compared, and the use of the crossover operator is investigated. The main contribution is not the result that the expected run time of the (1 + 1) evolutionary algorithm is Θ(n ln n) for separable functions with n variables but the methods by which this result can be proven rigorously.